Extending Horsetail Matching for Optimization Under Probabilistic, Interval, and Mixed Uncertainties
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optimization-under-uncertainty-cook-jarrett-willcox.pdf
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5.53 MB
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Author(s)
Cook, Laurence W.
Jarrett, Jerome P.
Willcox, Karen E
Date Issued
February 2018
Journal
AIAA Journal
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Citation
Cook, Laurence W., et al. “Extending Horsetail Matching for Optimization Under Probabilistic, Interval, and Mixed Uncertainties.” AIAA Journal, vol. 56, no. 2, Feb. 2018, pp. 849–61.
Version
Author's final manuscript
Abstract
This paper presents a new approach for optimization under uncertainty in the presence of probabilistic, interval, and mixed uncertainties, avoiding the need to specify probability distributions on uncertain parameters when such information is not readily available. Existing approaches for optimization under these types of uncertainty mostly rely on treating combinations of statistical moments as separate objectives, but this can give rise to stochastically dominated designs. Here, horsetail matching is extended for use with these types of uncertainties to overcome some of the limitations of existing approaches. The formulation delivers a single, differentiable metric as the objective function for optimization. It is demonstrated on algebraic test problems, the design of a wing using a low-fidelity coupled aerostructural code, and the aerodynamic shape optimization of a wing using computational fluid dynamics analysis.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.2514/1.J056371
